Overview

Dataset statistics

Number of variables14
Number of observations210
Missing cells609
Missing cells (%)20.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.1 KiB
Average record size in memory112.6 B

Variable types

NUM11
CAT3

Warnings

Country_Region has a high cardinality: 173 distinct values High cardinality
per_capita_exp_PPP_2016 is highly correlated with Health_exp_per_capita_USD_2016High correlation
Health_exp_per_capita_USD_2016 is highly correlated with per_capita_exp_PPP_2016High correlation
Country_Region has 23 (11.0%) missing values Missing
Province_State has 196 (93.3%) missing values Missing
Health_exp_pct_GDP_2016 has 24 (11.4%) missing values Missing
Health_exp_public_pct_2016 has 24 (11.4%) missing values Missing
Health_exp_out_of_pocket_pct_2016 has 24 (11.4%) missing values Missing
Health_exp_per_capita_USD_2016 has 24 (11.4%) missing values Missing
per_capita_exp_PPP_2016 has 24 (11.4%) missing values Missing
External_health_exp_pct_2016 has 43 (20.5%) missing values Missing
Physicians_per_1000_2009-18 has 21 (10.0%) missing values Missing
Nurse_midwife_per_1000_2009-18 has 21 (10.0%) missing values Missing
Specialist_surgical_per_1000_2008-18 has 35 (16.7%) missing values Missing
Completeness_of_birth_reg_2009-18 has 47 (22.4%) missing values Missing
Completeness_of_death_reg_2008-16 has 103 (49.0%) missing values Missing
Country_Region is uniformly distributed Uniform
Province_State is uniformly distributed Uniform
World_Bank_Name has unique values Unique
External_health_exp_pct_2016 has 36 (17.1%) zeros Zeros
Physicians_per_1000_2009-18 has 8 (3.8%) zeros Zeros

Reproduction

Analysis started2020-10-03 06:10:18.580675
Analysis finished2020-10-03 06:10:43.592393
Duration25.01 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Country_Region
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct173
Distinct (%)92.5%
Missing23
Missing (%)11.0%
Memory size1.6 KiB
United Kingdom
 
4
France
 
4
US
 
4
China
 
3
Denmark
 
3
Other values (168)
169 
ValueCountFrequency (%) 
United Kingdom41.9%
 
France41.9%
 
US41.9%
 
China31.4%
 
Denmark31.4%
 
Netherlands21.0%
 
Liechtenstein10.5%
 
Russia10.5%
 
Saint Kitts and Nevis10.5%
 
Burkina Faso10.5%
 
Other values (163)16377.6%
 
(Missing)2311.0%
 
2020-10-03T16:10:43.833977image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique167 ?
Unique (%)89.3%
2020-10-03T16:10:44.129898image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length7
Mean length7.661904762
Min length2

Province_State
Categorical

MISSING
UNIFORM

Distinct14
Distinct (%)100.0%
Missing196
Missing (%)93.3%
Memory size1.6 KiB
Cayman Islands
New Caledonia
Faroe Islands
Channel Islands
Guam
Other values (9)
ValueCountFrequency (%) 
Cayman Islands10.5%
 
New Caledonia10.5%
 
Faroe Islands10.5%
 
Channel Islands10.5%
 
Guam10.5%
 
Puerto Rico10.5%
 
Hong Kong10.5%
 
French Polynesia10.5%
 
Sint Maarten10.5%
 
Macau10.5%
 
Other values (4)41.9%
 
(Missing)19693.3%
 
2020-10-03T16:10:44.317700image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique14 ?
Unique (%)100.0%
2020-10-03T16:10:44.494999image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length3
Mean length3.538095238
Min length3

World_Bank_Name
Categorical

UNIQUE

Distinct210
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Zimbabwe
 
1
Channel Islands
 
1
Myanmar
 
1
Korea, Dem. People's Rep.
 
1
Georgia
 
1
Other values (205)
205 
ValueCountFrequency (%) 
Zimbabwe10.5%
 
Channel Islands10.5%
 
Myanmar10.5%
 
Korea, Dem. People's Rep.10.5%
 
Georgia10.5%
 
Papua New Guinea10.5%
 
Sint Maarten (Dutch part)10.5%
 
Tanzania10.5%
 
Mauritius10.5%
 
Croatia10.5%
 
Other values (200)20095.2%
 
2020-10-03T16:10:44.752450image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique210 ?
Unique (%)100.0%
2020-10-03T16:10:45.011835image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length8
Mean length9.657142857
Min length4

Health_exp_pct_GDP_2016
Real number (ℝ≥0)

MISSING

Distinct87
Distinct (%)46.8%
Missing24
Missing (%)11.4%
Infinite0
Infinite (%)0.0%
Mean6.715053763
Minimum1.7
Maximum23.3
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-10-03T16:10:45.265132image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1.7
5-th percentile3.1
Q14.5
median6.2
Q38.375
95-th percentile11.65
Maximum23.3
Range21.6
Interquartile range (IQR)3.875

Descriptive statistics

Standard deviation2.976536941
Coefficient of variation (CV)0.4432633074
Kurtosis5.310524432
Mean6.715053763
Median Absolute Deviation (MAD)1.85
Skewness1.583443965
Sum1249
Variance8.859772159
MonotocityNot monotonic
2020-10-03T16:10:45.539941image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6.262.9%
 
5.562.9%
 
6.152.4%
 
3.552.4%
 
3.952.4%
 
5.752.4%
 
6.741.9%
 
4.341.9%
 
10.441.9%
 
6.341.9%
 
Other values (77)13865.7%
 
(Missing)2411.4%
 
ValueCountFrequency (%) 
1.710.5%
 
210.5%
 
2.310.5%
 
2.421.0%
 
2.810.5%
 
ValueCountFrequency (%) 
23.310.5%
 
17.110.5%
 
16.510.5%
 
15.510.5%
 
12.610.5%
 

Health_exp_public_pct_2016
Real number (ℝ≥0)

MISSING

Distinct170
Distinct (%)91.4%
Missing24
Missing (%)11.4%
Infinite0
Infinite (%)0.0%
Mean52.91505376
Minimum5.1
Maximum96
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-10-03T16:10:45.847352image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum5.1
5-th percentile15
Q136.825
median55.85
Q370.6
95-th percentile83.825
Maximum96
Range90.9
Interquartile range (IQR)33.775

Descriptive statistics

Standard deviation21.95773555
Coefficient of variation (CV)0.4149619813
Kurtosis-0.926486375
Mean52.91505376
Median Absolute Deviation (MAD)16.55
Skewness-0.2735305125
Sum9842.2
Variance482.1421505
MonotocityNot monotonic
2020-10-03T16:10:46.135636image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
18.621.0%
 
61.421.0%
 
44.121.0%
 
60.621.0%
 
65.621.0%
 
84.121.0%
 
52.121.0%
 
79.621.0%
 
65.921.0%
 
45.921.0%
 
Other values (160)16679.0%
 
(Missing)2411.4%
 
ValueCountFrequency (%) 
5.110.5%
 
10.210.5%
 
11.210.5%
 
12.321.0%
 
1310.5%
 
ValueCountFrequency (%) 
9610.5%
 
94.910.5%
 
89.610.5%
 
89.110.5%
 
85.110.5%
 

Health_exp_out_of_pocket_pct_2016
Real number (ℝ≥0)

MISSING

Distinct165
Distinct (%)88.7%
Missing24
Missing (%)11.4%
Infinite0
Infinite (%)0.0%
Mean32.66182796
Minimum0.1
Maximum81
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-10-03T16:10:46.401861image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile7.25
Q116.2
median30.1
Q344.825
95-th percentile73.025
Maximum81
Range80.9
Interquartile range (IQR)28.625

Descriptive statistics

Standard deviation19.57277742
Coefficient of variation (CV)0.5992554198
Kurtosis-0.3485300567
Mean32.66182796
Median Absolute Deviation (MAD)14.5
Skewness0.5914849056
Sum6075.1
Variance383.0936161
MonotocityNot monotonic
2020-10-03T16:10:46.651840image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
44.631.4%
 
2831.4%
 
40.531.4%
 
18.931.4%
 
35.421.0%
 
27.721.0%
 
34.821.0%
 
35.921.0%
 
32.221.0%
 
37.421.0%
 
Other values (155)16277.1%
 
(Missing)2411.4%
 
ValueCountFrequency (%) 
0.110.5%
 
0.710.5%
 
2.110.5%
 
2.610.5%
 
4.610.5%
 
ValueCountFrequency (%) 
8110.5%
 
80.610.5%
 
78.910.5%
 
78.510.5%
 
77.410.5%
 

Health_exp_per_capita_USD_2016
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct185
Distinct (%)99.5%
Missing24
Missing (%)11.4%
Infinite0
Infinite (%)0.0%
Mean1037.004839
Minimum16.4
Maximum9869.7
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-10-03T16:10:46.908368image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum16.4
5-th percentile29.825
Q185.7
median322.6
Q31059.8
95-th percentile4735.075
Maximum9869.7
Range9853.3
Interquartile range (IQR)974.1

Descriptive statistics

Standard deviation1712.592621
Coefficient of variation (CV)1.651479875
Kurtosis8.346126487
Mean1037.004839
Median Absolute Deviation (MAD)267.4
Skewness2.712211421
Sum192882.9
Variance2932973.487
MonotocityNot monotonic
2020-10-03T16:10:47.168321image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
55.221.0%
 
327.210.5%
 
7477.910.5%
 
5063.610.5%
 
4714.310.5%
 
135.110.5%
 
2327.810.5%
 
422.810.5%
 
468.610.5%
 
37.710.5%
 
Other values (175)17583.3%
 
(Missing)2411.4%
 
ValueCountFrequency (%) 
16.410.5%
 
18.510.5%
 
19.210.5%
 
20.510.5%
 
20.910.5%
 
ValueCountFrequency (%) 
9869.710.5%
 
983610.5%
 
7477.910.5%
 
6271.410.5%
 
5710.610.5%
 

per_capita_exp_PPP_2016
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct186
Distinct (%)100.0%
Missing24
Missing (%)11.4%
Infinite0
Infinite (%)0.0%
Mean1412.466667
Minimum29.9
Maximum9869.7
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-10-03T16:10:47.428319image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum29.9
5-th percentile85.225
Q1231.5
median782.5
Q31885.5
95-th percentile5064.425
Maximum9869.7
Range9839.8
Interquartile range (IQR)1654

Descriptive statistics

Standard deviation1690.018939
Coefficient of variation (CV)1.196501821
Kurtosis4.256128905
Mean1412.466667
Median Absolute Deviation (MAD)632.95
Skewness1.935708653
Sum262718.8
Variance2856164.014
MonotocityNot monotonic
2020-10-03T16:10:47.677651image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3511.110.5%
 
493.710.5%
 
115.610.5%
 
90.610.5%
 
466.710.5%
 
162.810.5%
 
1193.110.5%
 
61.610.5%
 
400.310.5%
 
1577.910.5%
 
Other values (176)17683.8%
 
(Missing)2411.4%
 
ValueCountFrequency (%) 
29.910.5%
 
34.510.5%
 
50.310.5%
 
55.310.5%
 
61.410.5%
 
ValueCountFrequency (%) 
9869.710.5%
 
7867.410.5%
 
6374.210.5%
 
6203.510.5%
 
5463.310.5%
 

External_health_exp_pct_2016
Real number (ℝ≥0)

MISSING
ZEROS

Distinct89
Distinct (%)53.3%
Missing43
Missing (%)20.5%
Infinite0
Infinite (%)0.0%
Mean9.116766467
Minimum0
Maximum69.2
Zeros36
Zeros (%)17.1%
Memory size1.6 KiB
2020-10-03T16:10:47.922552image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median1.7
Q313.4
95-th percentile40.75
Maximum69.2
Range69.2
Interquartile range (IQR)13.2

Descriptive statistics

Standard deviation13.86825122
Coefficient of variation (CV)1.521180922
Kurtosis2.750307988
Mean9.116766467
Median Absolute Deviation (MAD)1.7
Skewness1.802556524
Sum1522.5
Variance192.3283919
MonotocityNot monotonic
2020-10-03T16:10:48.159277image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
03617.1%
 
0.273.3%
 
0.462.9%
 
1.262.9%
 
0.152.4%
 
0.741.9%
 
131.4%
 
0.331.4%
 
0.631.4%
 
2.331.4%
 
Other values (79)9143.3%
 
(Missing)4320.5%
 
ValueCountFrequency (%) 
03617.1%
 
0.152.4%
 
0.273.3%
 
0.331.4%
 
0.462.9%
 
ValueCountFrequency (%) 
69.210.5%
 
53.810.5%
 
50.610.5%
 
44.610.5%
 
43.810.5%
 

Physicians_per_1000_2009-18
Real number (ℝ≥0)

MISSING
ZEROS

Distinct50
Distinct (%)26.5%
Missing21
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean1.723280423
Minimum0
Maximum8.2
Zeros8
Zeros (%)3.8%
Memory size1.6 KiB
2020-10-03T16:10:48.402774image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.3
median1.3
Q32.8
95-th percentile4.42
Maximum8.2
Range8.2
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.570366481
Coefficient of variation (CV)0.9112657809
Kurtosis0.8919445669
Mean1.723280423
Median Absolute Deviation (MAD)1.1
Skewness0.9970496334
Sum325.7
Variance2.466050884
MonotocityNot monotonic
2020-10-03T16:10:48.621518image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.12110.0%
 
0.2167.6%
 
0.494.3%
 
0.883.8%
 
083.8%
 
2.462.9%
 
1.152.4%
 
0.352.4%
 
3.252.4%
 
2.352.4%
 
Other values (40)10148.1%
 
(Missing)2110.0%
 
ValueCountFrequency (%) 
083.8%
 
0.12110.0%
 
0.2167.6%
 
0.352.4%
 
0.494.3%
 
ValueCountFrequency (%) 
8.210.5%
 
6.610.5%
 
6.110.5%
 
5.410.5%
 
5.121.0%
 

Nurse_midwife_per_1000_2009-18
Real number (ℝ≥0)

MISSING

Distinct86
Distinct (%)45.5%
Missing21
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean4.139153439
Minimum0.1
Maximum20.3
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-10-03T16:10:48.831748image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q11.1
median2.8
Q36.1
95-th percentile11.86
Maximum20.3
Range20.2
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.965281687
Coefficient of variation (CV)0.9579934025
Kurtosis2.131372694
Mean4.139153439
Median Absolute Deviation (MAD)2
Skewness1.466345915
Sum782.3
Variance15.72345885
MonotocityNot monotonic
2020-10-03T16:10:49.392457image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.373.3%
 
0.973.3%
 
2.662.9%
 
1.462.9%
 
152.4%
 
0.552.4%
 
0.452.4%
 
1.252.4%
 
0.752.4%
 
441.9%
 
Other values (76)13463.8%
 
(Missing)2110.0%
 
ValueCountFrequency (%) 
0.131.4%
 
0.221.0%
 
0.373.3%
 
0.452.4%
 
0.552.4%
 
ValueCountFrequency (%) 
20.310.5%
 
18.110.5%
 
17.310.5%
 
15.710.5%
 
14.710.5%
 

Specialist_surgical_per_1000_2008-18
Real number (ℝ≥0)

MISSING

Distinct145
Distinct (%)82.9%
Missing35
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean37.93714286
Minimum0
Maximum195.6
Zeros1
Zeros (%)0.5%
Memory size1.6 KiB
2020-10-03T16:10:49.626967image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q13.1
median23.6
Q362.15
95-th percentile116.71
Maximum195.6
Range195.6
Interquartile range (IQR)59.05

Descriptive statistics

Standard deviation40.87752021
Coefficient of variation (CV)1.077506558
Kurtosis1.067918869
Mean37.93714286
Median Absolute Deviation (MAD)22.5
Skewness1.20837374
Sum6639
Variance1670.971658
MonotocityNot monotonic
2020-10-03T16:10:49.830833image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.452.4%
 
0.241.9%
 
0.541.9%
 
0.831.4%
 
50.131.4%
 
0.631.4%
 
1.131.4%
 
2.821.0%
 
11.421.0%
 
1.621.0%
 
Other values (135)14468.6%
 
(Missing)3516.7%
 
ValueCountFrequency (%) 
010.5%
 
0.110.5%
 
0.241.9%
 
0.321.0%
 
0.452.4%
 
ValueCountFrequency (%) 
195.610.5%
 
163.510.5%
 
156.810.5%
 
142.410.5%
 
133.310.5%
 

Completeness_of_birth_reg_2009-18
Real number (ℝ≥0)

MISSING

Distinct72
Distinct (%)44.2%
Missing47
Missing (%)22.4%
Infinite0
Infinite (%)0.0%
Mean84.20245399
Minimum2.7
Maximum100
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-10-03T16:10:50.042802image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile26.83
Q177.15
median96
Q3100
95-th percentile100
Maximum100
Range97.3
Interquartile range (IQR)22.85

Descriptive statistics

Standard deviation23.37002393
Coefficient of variation (CV)0.2775456394
Kurtosis1.850674571
Mean84.20245399
Median Absolute Deviation (MAD)4
Skewness-1.647932133
Sum13725
Variance546.1580186
MonotocityNot monotonic
2020-10-03T16:10:50.254342image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1006028.6%
 
99115.2%
 
9683.8%
 
9841.9%
 
8831.4%
 
7231.4%
 
7821.0%
 
9521.0%
 
9221.0%
 
8121.0%
 
Other values (62)6631.4%
 
(Missing)4722.4%
 
ValueCountFrequency (%) 
2.710.5%
 
11.310.5%
 
1210.5%
 
20.210.5%
 
2410.5%
 
ValueCountFrequency (%) 
1006028.6%
 
99.410.5%
 
99.310.5%
 
99115.2%
 
98.810.5%
 

Completeness_of_death_reg_2008-16
Real number (ℝ≥0)

MISSING

Distinct34
Distinct (%)31.8%
Missing103
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean89.30934579
Minimum4
Maximum100
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-10-03T16:10:50.443405image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile53.9
Q185.5
median99
Q3100
95-th percentile100
Maximum100
Range96
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation18.07115669
Coefficient of variation (CV)0.2023434002
Kurtosis7.573047768
Mean89.30934579
Median Absolute Deviation (MAD)1
Skewness-2.584789156
Sum9556.1
Variance326.5667043
MonotocityNot monotonic
2020-10-03T16:10:50.620171image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%) 
1004923.3%
 
9852.4%
 
9941.9%
 
9041.9%
 
9231.4%
 
9131.4%
 
8031.4%
 
7921.0%
 
8521.0%
 
7821.0%
 
Other values (24)3014.3%
 
(Missing)10349.0%
 
ValueCountFrequency (%) 
410.5%
 
1710.5%
 
24.310.5%
 
3710.5%
 
5110.5%
 
ValueCountFrequency (%) 
1004923.3%
 
99.910.5%
 
9941.9%
 
9852.4%
 
9610.5%
 

Interactions

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2020-10-03T16:10:41.471131image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-10-03T16:10:50.813681image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-03T16:10:51.168979image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-03T16:10:51.556935image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-03T16:10:51.933308image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-10-03T16:10:41.797246image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T16:10:42.325653image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T16:10:42.774880image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T16:10:43.331611image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

Country_RegionProvince_StateWorld_Bank_NameHealth_exp_pct_GDP_2016Health_exp_public_pct_2016Health_exp_out_of_pocket_pct_2016Health_exp_per_capita_USD_2016per_capita_exp_PPP_2016External_health_exp_pct_2016Physicians_per_1000_2009-18Nurse_midwife_per_1000_2009-18Specialist_surgical_per_1000_2008-18Completeness_of_birth_reg_2009-18Completeness_of_death_reg_2008-16
0AfghanistanNaNAfghanistan10.25.177.457.2162.817.50.30.30.042.3NaN
1AlbaniaNaNAlbania6.741.458.0271.5759.70.71.23.611.698.453.0
2AlgeriaNaNAlgeria6.667.730.9260.4998.20.01.82.212.1100.0NaN
3AndorraNaNAndorra10.449.141.73834.74978.7NaN3.34.083.1100.080.0
4AngolaNaNAngola2.944.135.295.2185.83.60.21.3NaN25.0NaN
5Antigua and BarbudaNaNAntigua and Barbuda4.360.632.2623.1976.40.02.83.114.0NaN79.0
6ArgentinaNaNArgentina7.574.415.8955.21531.00.64.02.650.1100.0100.0
7ArmeniaNaNArmenia9.916.580.6358.8876.91.72.95.686.799.376.0
8AustraliaNaNAustralia9.368.318.95002.44529.90.03.612.745.1100.0100.0
9AustriaNaNAustria10.472.518.94688.35295.2NaN5.18.2109.9100.0100.0

Last rows

Country_RegionProvince_StateWorld_Bank_NameHealth_exp_pct_GDP_2016Health_exp_public_pct_2016Health_exp_out_of_pocket_pct_2016Health_exp_per_capita_USD_2016per_capita_exp_PPP_2016External_health_exp_pct_2016Physicians_per_1000_2009-18Nurse_midwife_per_1000_2009-18Specialist_surgical_per_1000_2008-18Completeness_of_birth_reg_2009-18Completeness_of_death_reg_2008-16
200UruguayNaNUruguay9.171.717.41379.11958.90.05.01.938.9100.099.0
201UzbekistanNaNUzbekistan6.346.152.2135.1416.91.22.412.150.4NaNNaN
202NaNNaNVanuatu3.753.98.4109.8116.134.60.21.43.243.0NaN
203VenezuelaNaNVenezuela, RB3.224.140.01578.4940.00.0NaNNaN18.181.0100.0
204VietnamNaNVietnam5.747.444.6122.8356.32.30.81.4NaN96.0NaN
205USVirgin IslandsVirgin Islands (U.S.)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
206NaNNaNWest Bank and GazaNaNNaNNaNNaNNaNNaNNaNNaNNaN96.0NaN
207NaNNaNYemen, Rep.5.610.281.072.0144.57.80.30.70.830.7NaN
208ZambiaNaNZambia4.538.312.156.5175.242.50.10.91.511.3NaN
209ZimbabweNaNZimbabwe9.446.521.293.9185.025.40.11.21.638.0NaN